Evaluate a policy via Monte Carlo rollouts and produce a convergence report with DP-comparison if available. Use when you need help with mc evaluator.
Scanned 9/8/2026
Install to Claude Code
Skills Directorynpx -y skills add anubhavg-icpl/vibe --skill mc-evaluator --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Mc Evaluator?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/anubhavg-icpl-mc-evaluator)More formats (shields.io, HTML) on the badges page.
---
name: mc-evaluator
description: Evaluate a policy via Monte Carlo rollouts and produce a convergence report with DP-comparison if available. Use when you need help with mc evaluator.
license: CC-BY-NC-SA-4.0
phase: 9
lesson: 3
metadata:
version: 1.0.0
tags: [rl, monte-carlo, evaluation]
---
Given an environment (episodic, with reset+step API) and a policy, output:
1. Method. First-visit vs every-visit MC. Reason.
2. Episode budget. Target number, variance diagnostic, expected standard error.
3. Exploration plan. ε schedule (if needed) or exploring starts.
4. Gold-standard comparison. DP-optimal V* if tabular; otherwise a bound from a Q-learning / PPO baseline.
5. Termination check. Max-step cap, timeouts, handling of non-terminating trajectories.
Refuse to run MC on non-episodic tasks without a finite horizon cap. Refuse to report V^π estimates from fewer than 100 episodes per state for tabular tasks. Flag any policy with zero-variance actions as an exploration risk.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
Use this skill when developing or maintaining browser extension code in the `browser/` directory, including Chrome/Firefox/Edge compatibility, content scripts, background scripts, or i18n updates.
